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Kyrylo Kolodiazhnyi
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  • Kharkiv
  • Ukraine
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Kyrylo Kolodiazhnyi's Page

Profile Information

My Web Site Or LinkedIn Profile
http://www.linkedin.com/in/kyrylo-kolodiazhnyi-79b96914/
Field of Expertise
Machine Learning, Deep Learning
Professional Status
Technical
Your Company:
TeamDev
Industry:
Software development
Your Job Title:
Senior Software Engineer
How did you find out about DataScienceCentral?
From LinkedIn group
Interests:
Contributing, Networking

Kyrylo Kolodiazhnyi's Blog

Machine Learning with C++ - Mask R-CNN with PyTorch C++ Frontend

Posted on January 21, 2019 at 7:44am 0 Comments

I made C++ implementation of Mask R-CNN with PyTorch C++ frontend. The code is based on PyTorch implementations from multimodallearning and Keras implementation from …

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Machine Learning with C++ - Faster R-CNN with MXNet C++ Frontend

Posted on November 5, 2018 at 11:30pm 0 Comments

I published implementation of Faster R-CNN with MXNet C++ Frontend. You can use this implementation as comprehensive example of using MXNet C++ Frontend, it has custom data loader for MS Coco dataset, implements custom target proposal layer as a part of the project without modification MXNet library, contains code for errors checking (Missed in current C++ API), have Eigen and NDArray integration samples. Feel free to leave comments and proposes. The code is available on Github,…

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Machine Learning with C++ - Classification with Dlib

Posted on August 7, 2018 at 7:03am 0 Comments

Dlib is an open source C++ framework containing various machine learning algorithms and many other complementary stuff which can be used for image processing, computer vision, linear algebra calculations and many other things. It has very good documentation and a lot of useful examples. In this post I will show how to use this library for solving a classification problem on Iris data…

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Machine Learning with C++ - Classification with Shark-ML

Posted on July 30, 2018 at 2:40am 0 Comments

Shark-ML is an open-source machine learning library which offers a wide range of machine learning algorithms together with nice documentation, tutorials and samples. In this post I will show how to use this library for solving classification problem, with two different algorithms SVM and Random Forest. This post will tell you about how to use API for:

1. Loading data

2. Performing normalization and dimension…

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